Structural design, modeling and simulation analysis of a cage broiler inspection robot by Yongmin Guo, Xinwei Yu, Wanchao Zhang, Changxi Chen, Liji Yu

“…Additionally, the incorporation of a five-axis mechanical arm, integrated with sensors and a gimbal lifting algorithm, ensures adaptability to intricate inspection spaces, with a focus on energy efficiency. …”
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Using the LTO Network Level 1 Blockchain to Automate Inter-Organizational Business Processes by Khrypko Serhii L., Shcherbakov Serhii S.

“…Modeling live contracts as finite state machines allows visualizing them in the form of a flowchart. …”
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Design, Fabrication, and Application of Large-Area Flexible Pressure and Strain Sensor Arrays: A Review by Xikuan Zhang, Jin Chai, Yongfu Zhan, Danfeng Cui, Xin Wang, Libo Gao

“…Real-time data processing requires innovative solutions such as edge computing and machine learning algorithms, ensuring low-latency, high-accuracy data interpretation while preserving the flexibility of sensor arrays. …”
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Explainability of Network Intrusion Detection Using Transformers: A Packet-Level Approach by Pahavalan Rajkumardheivanayahi, Ryan Berry, Nicholas U. Costagliola, Lance Fiondella, Nathaniel D. Bastian, Gokhan Kul

“…Network Intrusion Detection Systems (NIDS) are critical in ensuring the security of connected computer systems by actively detecting and preventing unauthorized activities and malicious attacks. Machine learning based NIDS models leverage algorithms that learn from historical network traffic data to identify patterns and anomalies to capture complex relationships. …”
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HoRNS-CNN model: an energy-efficient fully homomorphic residue number system convolutional neural network model for privacy-preserving classification of dyslexia neural-biomarkers by Opeyemi Lateef Usman, Ravie Chandren Muniyandi, Khairuddin Omar, Mazlyfarina Mohamad, Ayoade Akeem Owoade, Morufat Adebola Kareem

“…Abstract Recent advancements in cloud-based machine learning (ML) now allow for the rapid and remote identification of neural-biomarkers associated with common neuro-developmental disorders from neuroimaging datasets. …”
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AI Driven Fraud Detection Models in Financial Networks: A Comprehensive Systematic Review by Nusrat Jahan Sarna, Farzana Ahmed Rithen, Umme Salma Jui, Sayma Belal, Al Amin, Tasnim Kabir Oishee, A. K. M. Muzahidul Islam

“…In response, this review paper explores the role of artificial intelligence (AI) in financial fraud detection, highlighting machine learning (ML), deep learning (DL), and hybrid models as transformative solutions. …”
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FLAML version 2.3.3 model-based assessment of gross primary productivity at forest, grassland, and cropland ecosystem sites by J. Lai, J. Lai, Y. Zhang, A. Wang, W. Fei, Y. Diao, R. Li, J. Wu

“…However, the variables and algorithms related to environmental limiting factors differ significantly across various LUE models, leading to high uncertainty in GPP estimation. …”
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Monitoring of greenhouse gas emission drivers in Atlantic Canadian Potato production: A robust explainable intelligent glass-box by Mehdi Jamei, Muhammad Hassan, Aitazaz A. Faroouqe, Mumtaz Ali, Masoud Karbasi, Gurjit S. Randhawa, Zaher Mundher Yaseen, Ross Dwyer

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Enhancing maize LAI estimation accuracy using unmanned aerial vehicle remote sensing and deep learning techniques by Zhen Chen, Weiguang Zhai, Qian Cheng

“…In Xuzhou, the R2 ranges from 0.60 to 0.83, the RMSE ranges from 0.46 to 0.71, and the rRMSE ranges from 10.96 % to 17.11 %. (2) The CNN model outperformed traditional machine learning algorithms in most cases. Moreover, the combination of spectral features, texture features, and crop height using the CNN model achieved the highest accuracy in LAI estimation, with the R2 ranging from 0.83 to 0.88, the RMSE ranging from 0.35 to 0.46, and the rRMSE ranging from 8.73 % to 10.96 %.…”
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Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry by Mathilde Duque, Cécile Emeraud, Rémy A. Bonnin, Quentin Giai-Gianetto, Laurent Dortet, Alexandre Godmer

“…Objectives: This study aims to improve the performance of MALDI-TOF for identifying Morganella spp. using WGS as the gold-standard reference method. Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. …”
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Root-Zone Salinity in Irrigated Arid Farmland: Revealing Driving Mechanisms of Dynamic Changes in China’s Manas River Basin over 20 Years by Guang Yang, Xuejin Qiao, Qiang Zuo, Jianchu Shi, Xun Wu, Alon Ben-Gal

“…The driving mechanisms behind root-zone <i>SSC</i> distributions were analyzed using an approach combined with two machine learning algorithms, eXtreme Gradient Boosting (XGBoost) and SHapley Additive exPlanation (SHAP), to identify influential factors and quantify their impacts. …”
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Harmonizing ground and UAV hyperspectral data: A novel spectral correction method for maximizing estimation models and datasets of ground hyperspectral by Zhonglin Wang, Pengxin Deng, Kairui Chen, Ying Xiong, Feng Yang, Cheng Wang, Zhixin Li, Biao Li, Yongjian Sun, Zongkui Chen, Zhiyuan Yang, Jun Ma

“…Estimation models of canopy nitrogen content (CNC) were developed using machine learning algorithms with non-imaging hyperspectral, hyperspectral images, and corrected hyperspectral datasets. …”
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Challenges for Competence-Oriented Education in the Context of the Development of Artificial Intelligence systems by Rodica Milena Zaharia

“…On one hand, education, in general, must be that process that ensures the skills that allow the creation of Artificial Intelligence. Algorithms, chatbots, learning machines were created as a result of knowledge, skills and abilities acquired through education. …”
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A Novel Data-Driven Method of Real-Time Transient Stability Assessment for AC/DC Hybrid Power Systems by Haifeng Li, Zhiwei Wang, Tao Jin, Xian Xu, Lin Shi

“…To effectively tackle the operational challenges facing today&#x2019;s bulk power systems with growing uncertainties, this paper presents a novel data-driven method for transient stability assessment method of the AC/DC hybrid power systems using auto-encoder-based algorithms for feature extraction and convolutional deep belief networks and Boltzmann machines for training accurate and robust models. …”
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Intelligent Recognition and Automated Production of Chili Peppers: A Review Addressing Varietal Diversity and Technological Requirements by Sheng Tai, Zhong Tang, Bin Li, Shiguo Wang, Xiaohu Guo

“…In terms of automation, we systematically discuss the principles and feasibility of different mechanized harvesting machines, consider the potential of vision-based keypoint detection for the point localization of picking, and explore motion planning and control for harvesting robots (e.g., robotic systems incorporating diverse end-effectors like soft grippers or cutting mechanisms and motion planning algorithms such as RRT) as well as seed cleaning/separation techniques and simulations (e.g., CFD and DEM) for equipment optimization. …”
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A survey on multi-agent reinforcement learning and its application by Zepeng Ning, Lihua Xie

“…Our survey also encompasses a detailed examination of benchmark environments used in MARL research, which are instrumental in evaluating MARL algorithms and demonstrate the adaptability of MARL to diverse application scenarios. …”
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A comparative analysis of fault detection and process diagnosis methods based on a signal processing paradigm by Dorel Aiordăchioaie

“…Finally, the considered methods are compared from the point of view of five criteria, namely, the recognition rate, window length, response time, computational resources, and complexity of the algorithms. A global quality criterion is built and used to assess the quality of the methods. …”
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A Smart Platform for Monitoring and Managing Energy Harvesting in Household Systems by Teodora Sanislav, George D. Mois, Sherali Zeadally, Silviu Folea, Horia Hedesiu

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